Data Engineer - Assistant Vice President
Citi (company site, opens in a new tab)3+ yrsPuneHybridDirect career pageEst. 20–35 LPA
Posted on 8 Sept
Reach people at Citi
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Citi you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design, develop, and deploy scalable batch and real-time data pipelines using Python, PySpark, Spark SQL, and Databricks.
- Manage and optimize Databricks cloud infrastructure on AWS or GCP including secure integrations and performance tuning using Delta Lake features.
- Lead technical best practices, mentoring, automated CI/CD pipeline establishment, and enforce data governance compliance including financial regulations.
Minimum Requirements
- Strong proficiency in Python (including pandas, pytest) and advanced SQL (window functions, CTEs, query optimization).
- Deep hands-on experience with Apache Spark (PySpark) and at least 3 years developing on Databricks, including Delta Lake ACID transactions and Unity Catalog.
- Experience deploying and managing Databricks environments on AWS or GCP with cloud-native components like S3/GCS, IAM, and related services.
- Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
- Experienced technical lead able to guide senior and mid-level engineers within Agile/Scrum environments.
- Expert in cloud-native data engineering with strong focus on Databricks lakehouse platform and distributed data processing.
- Skilled at collaborating cross-functionally with Data Science and BI teams to operationalize ML models and build semantic layers.